Urban Traffic Network Control in Smart Cities; a Distributed Model-based Control Approach
For smart city traffic management, this work offers a more efficient and constraint-satisfying distributed control method, though it is an incremental improvement over existing centralized approaches.
The paper proposes a distributed model predictive control approach for urban traffic networks, achieving 60% less computation time, 14.3% less total travel time, and 15.1% less queue length compared to centralized control while satisfying all constraints.
This paper proposes a distributed model predictive control (DMPC) approach for an urban traffic network (UTN) system. The control objective is to minimize the traffic congestion and the total travel time spent (TTS) in each link. The proposed DMPC algorithm considers traffic demand and disturbance predictions. The CasADi optimization tool is used to solve the constrained optimization problem. The proposed distributed control approach achieved 60% less computation time, 14.3% less TTS, and 15.1% less queue length compared to the centralized approach. Moreover, while the centralized algorithm neglected the input and state constraints, the distributed approach resulted in the satisfaction of all the constraints over the whole horizon.